Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis.

ABSTRACT: Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods implicitly test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty

SUBMITTER: Makrooni MA 

PROVIDER: S-EPMC9576052 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature